An Analysis of Parameters of Decomposition-Based MOEAs on Many-Objective Optimization
Elizabeth Montero, Saul Zapotecas Martinez · 2018
This paper presents an analysis of parameters defined within some of the most representative decomposition-based multi-objective evolutionary algorithms (MOEAs) namely: MOEA/D, MOEA/D-DE, and MOEA/D-DRA. Our main interest is focused in the many-objective context, where decomposition-based MOEAs have been successfully applied, but a lack of analysis on the relevance of their parameters is evidently observable. We review the literature related to those parameter values that have been commonly adopted and we perform some experiments oriented to validate these decisions. Our results show that some alternative parameter configurations can allow these methods to obtain better solutions than the standard values. Moreover, some of our recommendations can conduct to inspect in detail the design of these algorithms.